scholarly journals Corpus based Machine Translation System with Deep Neural Network for Sanskrit to Hindi Translation

2020 ◽  
Vol 167 ◽  
pp. 2534-2544
Author(s):  
Muskaan Singh ◽  
Ravinder Kumar ◽  
Inderveer Chana
Keyword(s):  
Neural Network ◽  
Machine Translation ◽  
Deep Neural Network ◽  
Translation System ◽  
Machine Translation System

scholarly journals Machine Translation of Concise State Sentences for Quantifier Phrases

2019 ◽  
Vol 1 (2) ◽  
pp. p138
Author(s):  
Wu Min
Keyword(s):  
Neural Network ◽  
Machine Translation ◽  
Deep Neural Network ◽  
Spoken Language ◽  
High Accuracy ◽  
Translation System ◽  
Machine Translation System ◽  
Quantifier Phrases ◽  
Translation Systems ◽  
Very High

With the development of deep learning methods, the machine translation system based on deep neural network has reached a very high accuracy, but for some daily Chinese phenomenon machine translation system is still not able to translate correctly. In this paper, we study a sentence that often appears in Chinese spoken language, that is, a simple state sentence composed of quantitative phrases, and improve the existing machine translation system. The external helper program constructed in this paper is compatible with the current mainstream network translation systems, greatly improving the translation effect of these translation systems on the concise state sentences composed of quantitative phrases.

scholarly journals A Chaotic Neural Network Model for English Machine Translation Based on Big Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Qianyu Cao ◽  
Hanmei Hao
Keyword(s):  
Neural Network ◽  
Big Data ◽  
Data Analysis ◽  
Machine Translation ◽  
Big Data Analysis ◽  
Translation System ◽  
Source Text ◽  
Chaotic Neural Network ◽  
Passive Voice ◽  
Machine Translation System

In this paper, the chaotic neural network model of big data analysis is used to conduct in-depth analysis and research on the English translation. Firstly, under the guidance of the translation strategy of text type theory, the translation generated by the machine translation system is edited after translation, and then professionals specializing in computer and translation are invited to confirm the translation. After that, the errors in the translations generated by the machine translation system are classified based on the Double Quantum Filter-Muttahida Quami Movement (DQF-MQM) error type classification framework. Due to the characteristics of the source text as an informative academic text, long and difficult sentences, passive voice, and terminology translation are the main causes of machine translation errors. In view of the rigorous logic of the source text and the fixed language steps, this research proposes corresponding post-translation editing strategies for each type of error. It is suggested that translators should maintain the logic of the source text by converting implicit connections into explicit connections, maintain the academic accuracy of the source text by adding subjects and adjusting the word order to deal with the passive voice, and deal with semitechnical terms by appropriately selecting word meanings in postediting. The errors of machine translation in computer science and technology text abstracts are systematically categorized, and the corresponding post-translation editing strategies are proposed to provide reference suggestions for translators in this field, to improve the quality of machine translation in this field.

scholarly journals Quantum Neural Network Based Machine Translator for Hindi to English

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Ravi Narayan ◽  
V. P. Singh ◽  
S. Chakraverty
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Machine Translation ◽  
Translation System ◽  
Quantum Neural Network ◽  
Individual Word ◽  
Part Of Speech ◽  
Machine Translation System ◽  
Simulation And Evaluation

This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus. The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus, using the information of part of speech of individual word in the corpus, like a human. The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English. To analyze the effectiveness of the proposed approach, 2600 sentences have been evaluated during simulation and evaluation. The accuracy achieved on BLEU score is 0.7502, on NIST score is 6.5773, on ROUGE-L score is 0.9233, and on METEOR score is 0.5456, which is significantly higher in comparison with Google Translation and Bing Translation for Hindi to English Machine Translation.

English-Dogri Translation System using MOSES

2016 ◽  
Vol 1 (1) ◽  
pp. 45-49
Author(s):  
Avinash Singh ◽  
Asmeet Kour ◽  
Shubhnandan S. Jamwal
Keyword(s):  
Natural Language Processing ◽  
Machine Translation ◽  
Language Processing ◽  
Statistical Machine Translation ◽  
Translation System ◽  
Parallel Corpus ◽  
English System ◽  
Machine Translation System ◽  
Translation Machine ◽  
Language Pair

The objective behind this paper is to analyze the English-Dogri parallel corpus translation. Machine translation is the translation from one language into another language. Machine translation is the biggest application of the Natural Language Processing (NLP). Moses is statistical machine translation system allow to train translation models for any language pair. We have developed translation system using Statistical based approach which helps in translating English to Dogri and vice versa. The parallel corpus consists of 98,973 sentences. The system gives accuracy of 80% in translating English to Dogri and the system gives accuracy of 87% in translating Dogri to English system.

scholarly journals Efficient Embedded Decoding of Neural Network Language Models in a Machine Translation System

2018 ◽  
Vol 28 (09) ◽  
pp. 1850007
Author(s):  
Francisco Zamora-Martinez ◽  
Maria Jose Castro-Bleda
Keyword(s):  
Neural Network ◽  
Machine Translation ◽  
Language Processing ◽  
Traditional Approach ◽  
Computational Cost ◽  
Integrated Approach ◽  
Language Models ◽  
Translation System ◽  
Neural Net ◽  
Network Language

Neural Network Language Models (NNLMs) are a successful approach to Natural Language Processing tasks, such as Machine Translation. We introduce in this work a Statistical Machine Translation (SMT) system which fully integrates NNLMs in the decoding stage, breaking the traditional approach based on [Formula: see text]-best list rescoring. The neural net models (both language models (LMs) and translation models) are fully coupled in the decoding stage, allowing to more strongly influence the translation quality. Computational issues were solved by using a novel idea based on memorization and smoothing of the softmax constants to avoid their computation, which introduces a trade-off between LM quality and computational cost. These ideas were studied in a machine translation task with different combinations of neural networks used both as translation models and as target LMs, comparing phrase-based and [Formula: see text]-gram-based systems, showing that the integrated approach seems more promising for [Formula: see text]-gram-based systems, even with nonfull-quality NNLMs.

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